PID landing orbit motion controller for an indoor blimp robot

Blimp robots are attractive as indoor flying robots because they can float in the air, land safely with low energy, and stay in motion for a long time compared with other flying robots. However, controlling blimp robots is difficult because they have nonlinear characteristics, are influenced by air streams, and can easily be influenced by inertia. Therefore, a robust and adaptive control system is needed for blimp robots. The applied research that has studied the features of indoor flying robots in recent years has prospered. Operating an indoor blimp robot for a long time is difficult because the payload is small, multiple batteries cannot be stacked, and the design of a thruster that gives freedom to the entire blimp robot is difficult. Therefore, an autonomous charge that allows operation for a long time is needed. We have developed a method of landing with orbital control of the charge point that gives autonomy to a blimp robot. The possibility of landing with orbital control is shown. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005 An erratum to this article is available at .     

基于Boltzmann 机神经网络认知机制的机器人趋光控制

针对移动机器人未知环境下的趋光控制问题,模拟人或动物“感知-行动”认知机制,对具有趋光特性的移动机器人进行设计,提出一种基于Boltzmann机神经网络的趋光控制方法。该方法首先应用知识集对机器人趋光控制器的Boltzmann机神经网络进行趋光训练;然后应用Boltzmann机神经网络的运行机制实现趋光控制。仿真实验表明,该方法能够提高机器人学习的控制精度。     

Learning landing control of an indoor blimp robot for self-energy recharging

We report on learning landing control for the self-energy recharging of indoor blimp robots. Indoor blimp robots have potential applications in monitoring, surveillance, and entertainment. It might be necessary for blimp robots to fly for a long time to achieve these tasks. Since blimp robots cannot have a heavy battery, it is difficult to fly for long time. Therefore, we solved this problem by developing self-energy recharging control, i.e., docking to an energy charging station. We call it landing control. We introduce a way to learn landing control to tackle an unstable flying condition. The results of experiments show the effectiveness of the landing control. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

基于Netvlad神经网络的室内机器人全局重定位方法

针对已知地图的室内机器人全局重定位、绑架恢复问题,提出一种基于改进的Netvlad卷积神经网络的室内机器人全局重定位方法,通过激光雷达获取的障碍物信息引导机器人到达空旷区域,粗定位阶段,使用栅格地图最短连通域距离作为正样本判据,并对Netvlad引入残差网络,通过图像检索得到机器人的粗略位置及角度信息。使用粗定位阶段得到的位置和角度信息作为自适应蒙特卡罗定位的初始值来估计机器人的精确位姿。实验结果表明,与传统定位方法相比,该方法可以使机器人从绑架问题中快速恢复准确位姿。     

Feature extraction method for a robot map using neural networks

Many map-building algorithms using ultrasonic sensors have been developed for mobile robot applications. In indoor environments, the ultrasonic sensor system gives some uncertain data. To compensate for this effect, a new feature extraction method using neural networks is proposed. A new, effective representation of the target is defined, and the reflection wave data patterns are learnt using neural networks. As a consequence, the targets are classified as planes, corners, or edges, which all frequently occur in indoor environments. We constructed our own robot system for the experiments which were carried out to show the performance. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

含未知动态与扰动的非线性系统神经网络嵌入学习控制

针对带有不确定性与扰动的非线性系统的性能优化问题, 提出一种基于神经网络嵌入的学习控制方法. 对一类常见的 Lyapunov 函数导数形式, 将神经网络控制器集成到某种对系统稳定的基准控制器中, 其意义在于将原控制器改进为满足Lyapunov稳定的神经网络参数可调控制器, 从而能够利用先进的神经网络学习技术实现控制器的在线优化. 建立了跟踪误差的等效目标函数, 避免了对系统输入–输出的辨识问题. 建立了一种未知非线性与扰动等效值自适应方法, 并依此方法设计基准控制器. 以RBF (Radial basis function) 反步自适应控制、基于卷积神经网络的滑模控制和深度强化学习控制为对比方法, 对带有死区、饱和、三角函数等数值与物理非线性模型进行仿真分析以测试方法有效性, 并针对上肢康复机器人控制问题进行虚拟实验以验证该方法的实用性. 仿真与实验结果表明, 该方法能在Lyapunov 稳定条件下有效优化基础控制器性能, 对比结果证实了该方法的实用性与先进性.     

An Adaptive Actor-critic Algorithm with Multi-step Simulated Experiences for Controlling Nonholonomic Mobile Robots

In this paper, we propose a new algorithm of an adaptive actor-critic method with multi-step simulated experiences, as a kind of temporal difference (TD) method. In our approach, the TD-error is composed of two value- functions and m utility functions, where m denotes the number of multi-steps in which the experience should be simulated. The value-function is constructed from the critic formulated by a radial basis function neural network (RBFNN), which has a simulated experience as an input, generated from a predictive model based on a kinematic model. Thus, since our approach assumes that the model is available to simulate the m-step experiences and to design a controller, such a kinematic model is also applied to construct the actor and the resultant model based actor (MBA) is also regarded as a network, i.e., it is just viewed as a resolved velocity control network. We implement this approach to control nonholonomic mobile robot, especially in a trajectory tracking control problem for the position coordinates and azimuth. Some simulations show the effectiveness of the proposed method for controlling a mobile robot with two-independent driving wheels.     

Comments on ‘Differentiator application in altitude control for an indoor blimp robot’

ABSTRACT

Comparison of several types of differentiation algorithms has been performed in the paper Wang, Zheng, Efimov, and Perruquetti [(2018). Differentiator application in altitude control for an indoor blimp robot. International Journal of Control, 1–10. Retrieved from https://doi.org/10.1080/00207179.2018.1441549] with application to a robotic blimp altitude control. Unfortunately, one of the differentiation methods, the algebraic differentiator, has not been applied properly. The clarifications and the new comparison results are given in this note.     

基于迁移学习的移动机器人单帧图像坡度检测算法

针对未知环境下移动机器人平稳上坡控制对坡度感知精度的要求,本文提出了一种基于迁移学习的移动机器人单帧图像坡度检测算法.利用室内图像标准数据集训练深度卷积神经场-全连接超像素池化网络(deep convolutional neural field-fully connected superpixel pooling ne...     

基于BP神经网络的移动机器人避障设计及仿真

为了解决移动机器人在复杂环境中如何高效精确地躲避障碍物的问题,提出了一种基于BP神经网络的避障方法。建立了机器人的避障运动模型并设计了神经网络避障控制系统;分析了机器人在运动过程中与障碍物的位置关系,使用超声波传感器采集距离信息,进行BP神经网络输入、输出训练并采用Matlab工具进行仿真试验。结果表明,该方法可以高效精确地实现移动机器人的自主避障,运行相对稳定、轨迹连续平滑,达到了较为理想的避障效果。验证了方法的可行性和有效性,为移动机器人自主避障提供了一种新的控制方法。     

Detection and prevention of slip using sensors with different properties embedded in elastic artificial skin on the basis of previous experience

In dexterous robotic manipulation, it is essential to control the force exerted by the robot hands while grasping. This paper describes a method by which robot hands can be controlled on the basis of previous experience of slippage of objects held by the hand. We developed an anthropomorphic human scale robot hand equipped with an elastic skin in which two types of sensor are randomly embedded. One of these sensors is a piezoelectric polyvinylidenefluoride (PVDF) film which can be used for the detection of pressure changes. The other is a strain gauge which can measure static pressure. In our system, PVDF films are used to detect slipping, and strain gauges to measure stresses which are caused by normal and shear forces. The stress measured by the strain gauges is used as input data to a neural network which controls the actuators of the robot. Once slip is detected, the neural network is updated to prevent it. We show that this system can control the grasp force of the robot hand and adapt it to the weight of the object. By using this method, it was shown that robots can hold objects safely.     

基于改进型BP网络的并联机器人自适应力控制

并联机器人力控制是并联机器人研究的一个热点和难点,引起了许多学者的关注,并取得了一定的成果。多数使用了传统的力控制研究方法。该文中,作者将神经网络引入并联机器人的力控制中,并介绍了一种改进型BP神经网络,以及其学习算法和网络的训练过程,并结合实际并联机器人6-SPS并联机器人,设计出基于改进型BP神经网络的并联机器人自适应力控制器,并进行了仿真和实验研究,通过研究表明所设计的控制器是可行和有效的。     

Asymptotically stable visual servoing of manipulators via neural networks

In this article we present a class of position control schemes for robot manipulators based on feedback of visual information processed through artificial neural networks. We exploit the approximation capabilities of neural networks to avoid the computation of the robot inverse kinematics as well as the inverse task space–camera mapping which involves tedious calibration procedures. Our main stability result establishes rigorously that in spite of the neural network giving an approximation of these mappings, the closed‐loop system including the robot nonlinear dynamics is locally asymptotically stable provided that the Jacobian of the neural network is nonsingular. The feasibility of the proposed neural controller is illustrated through experiments on a planar robot. © 2000 John Wiley & Sons, Inc.     

Application of a neural network to the generation of a robot arm trajectory

We propose a neural network model generating a robot arm trajectory. The developed neural network model is based on a recurrent-type neural network (RNN) model calculating the proper arm trajectory based on data acquired by evaluation functions of human operations as the training data. A self-learning function has been added to the RNN model. The proposed method is applied to a 2-DOF robot arm, and laboratory experiments were executed to show the effectiveness of the proposed method. Through experiments, it is verified that the proposed model can reproduce the arm trajectory generated by a human. Further, the trajectory of a robot arm is successfully modified to avoid collisions with obstacles by a self-learning function.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

Robust Recurrent Neural Network Control of Biped Robot

In this paper, a recurrent neural network (RNN) control scheme is proposed for a biped robot trajectory tracking system. An adaptive online training algorithm is optimized to improve the transient response of the network via so-called conic sector theorem. Furthermore, L ₂-stability of weight estimation error of RNN is guaranteed such that the robustness of the controller is ensured in the presence of uncertainties. In consideration of practical applications, the algorithm is developed in the discrete-time domain. Simulations for a seven-link robot model are presented to justify the advantage of the proposed approach. We give comparisons between the standard PD control and the proposed RNN compensation method.     

An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation

A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.     

A robust adaptive control of a parallel robot

The work presented in this article deals with the robust adaptive control tracking of a 6 degree of freedom parallel robot, called C5 parallel robot. The proposed approach is based on the coupling of sliding modes and multi-layers perceptron neural networks (MLP-NNs). It does not require the inverse dynamic model for deriving the control law. The MLP-NN is added in the control scheme to estimate the gravitational and frictional forces along with the non-modelled dynamic effects. The nonlinearity problem, present in neural networks, is resolved using Taylor series expansion. The proposed approach allows to adjust the parameters of neural network and sliding mode control terms by taking into account a reference model and the closed-loop stability in the Lyapunov sense. We implemented our approach on the C5 parallel robot of LISSI laboratory and performed experiments to observe its effectiveness and the robust behaviour of the controller against external disturbances.     

Reinforcement learning for a biped robot to climb sloping surfaces

A neural network mechanism is proposed to modify the gait of a biped robot that walks on sloping surfaces using sensory inputs. The robot climbs a sloping surface from a level surface with no priori knowledge of the inclination of the surface. By training the neural network while the robot is walking, the robot adjusts its gait and finally forms a gait that is as stable as when it walks on the level surface. The neural network is trained by a reinforcement learning mechanism while proportional and integral (PI) control is used for position control of the robot joints. Experiments of static and pseudo dynamic learning are performed to show the validity of the proposed reinforcement learning mechanism. © 1997 John Wiley & Sons, Inc.     

基于Labview的舵机驱动爬壁机器人CPG神经网络运动控制的实现研究

针对舵机驱动爬壁机器人的机构特点,提出一种用Labview实现舵机驱动爬壁机器人CPG神经网络运动控制的方法．首先,基于仿生运动控制的概念构建出舵机驱动爬壁机器人神经网络运动控制模型．然后,将机器人的平面自由运动分解成直线运动和转弯运动的组合,并结合CPG神经网络信号波形特点,完成机器人相应的实际运动控制信号的生成与输出．最后,通过机器人平面运动控制的实验研究,验证了所提控制方法的有效性．     

Neural network-based self-learning control for power transmission line deicing robot

Recently, the application of the maintenance transmission line robot has been very popular in the power industry. However, difficulties in the control of maintenance transmission line robot exist due to multiple nonlinearities, plant parameter variations and external disturbances. This paper investigates the possibility of using neural network as a promising self-learning control alternative for the control problem of inspection and deicing transmission line robot. We first discuss the mechanical structure, as well as dynamic model of a deicing robot. And then, a neural network-based self-learning control strategy consists of a fuzzy neural network controller and an ELM-based single-layer-feedback neural networks identifier are proposed for this deicing transmission line robot. Both the structure and the learning algorithm of the control system are presented. The proposed controller is verified by computer simulations and experiments.